Skip Navigation



Bioinformatics Advance Access published online on March 29, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti397
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary data
Right arrow All Versions of this Article:
21/11/2674    most recent
bti397v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Tarca, A. L.
Right arrow Articles by Mackay, J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Tarca, A. L.
Right arrow Articles by Mackay, J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received September 10, 2004
Revised March 17, 2005
Accepted March 18, 2005

Article

A robust neural networks approach for spatial and intensity dependent normalization of cDNA microarray data

A. L. Tarca 1*, J. E. K. Cooke 1, and J. Mackay 1

1 Research Center in Forest Biology, Department of Wood and Forest Science, Laval University, Sainte-Foy (QC), G1K-7P4, Canada

* To whom correspondence should be addressed.
A. L. Tarca, E-mail: ltarca{at}rsvs.ulaval.ca


  Abstract

Motivation: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-colour) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias.

Results: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray data sets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as the spatial coordinates (X, Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudo spatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.

Availability: The normalization method described in this paper is available as the library nnNorm in the BioConductor project (http://www.bioconductor.org). Scripts used to load the publicly available data and generate some of the figures in this paper are available in the documentation accompanying this library.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
D. S. Yuan and R. A. Irizarry
High-resolution spatial normalization for microarrays containing embedded technical replicates
Bioinformatics, December 15, 2006; 22(24): 3054 - 3060.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.